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Updated: Jul 11, 2025

Author Spotlight: Harnessing DNA Barcode Technology to Enhance the Efficiency of Medicinal Plant Identification
Published on: November 1, 2024
The DNA barcode identification of Dalbergia odorifera T. Chen and Dalbergia tonkinensis Prain
Weijie Wang1, Baixu Chen1, Ruoke Ma1
1Guangxi Key Laboratory of Forest Ecology and Conservation, Key Laboratory of National Forestry and Grassland Administration on Cultivation of Fast-Growing Timber in Central South China, College of Forestry, Guangxi University, Nanning, 530004, China.
Background:
Dalbergia odorifera is a precious tree species with unique economic and medicinal values, which is difficult to distinguish from Dalbergia tonkinensis by traditional identification methods such as morphological characteristics and wood structure characteristics. It has been demonstrated that the identification of tree species can be effectively achieved using DNA barcoding, but there is a lack of study of the combined sequences used as DNA barcodes in the two tree species. In this study, 10 single sequences and 4 combined sequences were selected for analysis, and the identification effect of each sequence was evaluated by the distance-based method, BLAST-based search, character-based method, and tree-based method.
Results:
Among the single sequences and the combined sequences, the interspecies distance of trnH-psbA and ITS2 + trnH-psbA was greater than the intraspecies distance, and there was no overlap in their frequency distribution plots. The results of the Wilcoxon signed-rank test for the interspecies distance of each sequence showed that the interspecies differences of the single sequences except trnL-trnF, trnH-psbA, and ycf3 were significantly smaller than those of the combined sequences. The results of BLAST analysis showed that trnH-psbA could accurately identify D. odorifera and D. tonkinensis at the species level. In the character-based method, single sequences of trnL-trnF, trnH-psbA with all the combined sequences can be used for the identification of D. odorifera and D. tonkinensis. In addition, the neighbor-joining (NJ) trees constructed based on trnH-psbA and ITS2 + trnH-psbA were able to cluster D. odorifera and D. tonkinensis on two clades.
Conclusions:
The results showed that the character-based method with the BLOG algorithm was the most effective among all the evaluation methods, and the combined sequences can improve the ability to identify tree species compared with single sequences. Finally, the trnH-psbA and ITS2 + trnH-psbA were proposed as DNA barcodes to identify D. odorifera and D. tonkinensis.
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